Advertising spots on TV and radio are key marketing tools used by companies to drive web page traffic and sales. These companies often wish to determine the effect of individual advertising spots on web page traffic and sales. Specifically, for various reasons, it is desirable to measure usage of online network-connected resources, and to attribute portions of such use to offline stimuli, such as TV or radio advertisements.
Accuracy in attributing web traffic and sales to specific advertising spots is important to such companies. However, measuring the effectiveness of TV advertisements is far more challenging than with online ads. Similarly, optimizing ads to a target demographic is far more difficult with TV than with online media. Customers almost always view ads on TV and make purchases (i.e., “conversions”) through other channels, including through the Internet.
The most common industry approach for understanding who is viewing TV advertisements is by using viewer panels of volunteer users who allow their activities to be monitored. However, the industry standard Nielsen panel contains only 25,000 users (out of approximately 114.5 million television households), which is less than 0.022% of population.
Aside from the use of the Nielsen panel, other solutions for identifying peaks in web traffic traditionally involved analyzing web traffic as a function of a standard deviation from a local average of traffic volume. For example, in some conventional solutions, a simple threshold technique is used to define all traffic above a threshold value multiplied by the standard deviation from a local average trend line. Unfortunately, both of these local average and standard deviation metrics are easily skewed when there is a spike in traffic. Moreover, this technique only captures the tip of a peak, but fails to identify and attribute the base of the peak in online traffic. Thus, there exists a need for a method to accurately identify and attribute peaks in online traffic to advertising spots.
Further, conventional solutions have no way of attributing a single peak to multiple advertising spots that aired at similar times. Thus, there exists a need for a method to accurately divide peaks in online traffic, and to attribute online traffic and sales between multiple advertising spots that aired at similar times.